Henry Frank Seminar Lecture 2 - Benoit Roux - University of Chicago
- Peng Liu
- Feb 28
- 2 min read
February 28, 2025 - 3:00pm to 4:00pm
Title: Rare Conformational Transitions in Biomolecular Systems
Abstract: Classical molecular dynamics (MD) simulations based on atomic models play an increasingly important role in a wide range of applications in physics, biology and chemistry. One of the most difficult problems is the characterization of large conformational transitions occurring over long-time scales. A significant challenge is to sample the transitions between metastable states associated with slow molecular processes. Most computational strategies require the knowledge of a suitable reaction-coordinate, which have traditionally been constructed using human intuition. To tackle increasingly difficult problems, it is important to develop more objectively robust approaches. Transition path theory, combining free energy methods, string method, transition pathway techniques, stochastic Markov State Models, and Machine Learning techniques based on artificial Neural Networks, offers a powerful paradigm to address these issues.1-6 These concepts will be formally introduced and illustrated with a few recent computational studies of biomolecular systems.
References for both Lectures:
A. C. Pan, D. Sezer & B. Roux. Finding transition pathways using the string method with swarms of trajectories, J. Phys. Chem. B 112, 3432-3440, (2008).
A. C. Pan & B. Roux. Building Markov state models along pathways to determine free energies and rates of transitions, J. Chem. Phys. 129, 064107, (2008).
B. Roux. String Method with Swarms-of-Trajectories, Mean Drifts, Lag Time, and Committor, J. Phys. Chem. A 125, 7558-7571, (2021).
B. Roux. Transition rate theory, spectral analysis, and reactive paths, J. Chem. Phys. 156, 134111, (2022),
Z. He, C. Chipot & B. Roux. Committor-Consistent Variational String Method, J. Phys. Chem. Lett. 13, 9263−9271, (2022).
H. Chen, B. Roux & C. Chipot. Discovering Reaction Pathways, Slow Variables, and Committor Probabilities with Machine Learning, Journal of chemical theory and computation 19, 4414-4426, (2023).
See more of Dr. Roux's research on his website: https://chemistry.uchicago.edu/faculty/beno%C3%AEt-roux
Location and Address
Chevron 150